Enhancing Business Support Systems through Data Science and Machine Learning : A study on possible applications within BSS

Abstract: The companies’ support phase, as all of business’ functional areas and components, went through a heavy and rapid digitalization which has unlocked the availability of an unprecedented amount of data. Unlike other relevant business areas and components, the support phase seems to have experienced fewer improvements attributable to Data Science and machine learning. By focusing on two well-known problems of these two fields, Time Series Analysis and Regression Analysis, this project aims at understanding which techniques are applicable within the support phase and how these can improve the effectiveness and pro-activeness of this area. The goal within this project is to apply them to improve the handling of support tickets, the digital entity used to track issues and requests within support systems. Through the use of Time Series Analysis, we aim at forecasting the volume of tickets to be expected in a near-future time frame. Using Regression Analysis we intend to estimate the resolution time of a newly submitted ticket. The results produced by the two tasks were satisfactory. On one hand, the Time Series task produced accurate results and the models could be directly employed and bring some added value to help Elvenite’s support team. On the other hand, while the Regression Analysis results were not as good, they nonetheless proved that the task’s aim is achievable through improvements on both the data used and the models applied. Finally, both tasks successfully showcased how to investigate and evaluate the application of such techniques within the support phase of a business.